gluonts-forecasting-notebooks-1/PyTorch_ts_time_series_forecasting(gluonts).ipynb

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  "metadata": {
    "colab": {
      "name": "PyTorch-ts time series forecasting(gluonts).ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNsJ6L3eNGqQqGKIupo8kON",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/mmaithani/data-science/blob/main/PyTorch_ts_time_series_forecasting(gluonts).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cqQAiYfhk2Ic",
        "outputId": "d81fd16a-a8b2-4e16-acea-1c65603bd491"
      },
      "source": [
        "pip install pytorchts"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pytorchts\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/37/90/4a1fb6661806b4d706d5c7d2a7abea8867d55b819d80132179e95ba27a8d/pytorchts-0.2.0.tar.gz (124kB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c0/95/cb9820560a2713384ef49060b0087dfa2591c6db6f240215c2bce1f4211c/pandas-1.0.5-cp36-cp36m-manylinux1_x86_64.whl (10.1MB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/52/ea/fae9f69b6e56407961318e8c73e203097a97c7bd71b30bf1b4f5eb448f28/pydantic-1.7.3-cp36-cp36m-manylinux2014_x86_64.whl (9.2MB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a0/8f/50488049033aba6943659d7906a45947ea5b510b74e7d44ab9d7457e1710/python_rapidjson-1.0-cp36-cp36m-manylinux2010_x86_64.whl (1.4MB)\n",
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            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->pytorchts) (3.1.0)\n",
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            "Building wheels for collected packages: pytorchts\n",
            "  Building wheel for pytorchts (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pytorchts: filename=pytorchts-0.2.0-cp36-none-any.whl size=160984 sha256=42c1d98437121b8a1d47acd8306f2e8bfd4217bac3131293bfd4e20f1afb9ff8\n",
            "  Stored in directory: /root/.cache/pip/wheels/12/ee/34/9834a5114abdb5a1ae388b778b29847380e093e16de7f172d8\n",
            "Successfully built pytorchts\n",
            "\u001b[31mERROR: google-colab 1.0.0 has requirement pandas~=1.1.0; python_version >= \"3.0\", but you'll have pandas 1.0.5 which is incompatible.\u001b[0m\n",
            "Installing collected packages: pandas, pydantic, python-rapidjson, pytorchts\n",
            "  Found existing installation: pandas 1.1.5\n",
            "    Uninstalling pandas-1.1.5:\n",
            "      Successfully uninstalled pandas-1.1.5\n",
            "Successfully installed pandas-1.0.5 pydantic-1.7.3 python-rapidjson-1.0 pytorchts-0.2.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "pandas"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nGRKmXXdk5X-"
      },
      "source": [
        "import matplotlib.pyplot as plt\r\n",
        "import pandas as pd\r\n",
        "import torch\r\n",
        "\r\n",
        "from pts.dataset import ListDataset\r\n",
        "from pts.model.deepar import DeepAREstimator\r\n",
        "from pts import Trainer\r\n",
        "from pts.dataset import to_pandas"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "4JKU3tqzlCXq",
        "outputId": "7ec3725e-5caf-44c2-9a45-3aa824efd559"
      },
      "source": [
        "url = \"https://raw.githubusercontent.com/numenta/NAB/master/data/realTweets/Twitter_volume_AMZN.csv\"\r\n",
        "df = pd.read_csv(url, header=0, index_col=0, parse_dates=True)\r\n",
        "df.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>2015-02-26 21:42:53</th>\n",
              "      <td>57</td>\n",
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              "      <th>2015-02-26 21:47:53</th>\n",
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              "      <th>2015-02-26 22:02:53</th>\n",
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              "                     value\n",
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              "2015-02-26 22:02:53     93"
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          "metadata": {
            "tags": []
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          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
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        "id": "clwpRjeYlGmf",
        "outputId": "1f6eb718-a33e-4083-9704-13cb57f9b2ca"
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      "source": [
        "df[:100].plot(linewidth=2)\r\n",
        "plt.grid(which='both')\r\n",
        "plt.show()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jTIYIKyClTam"
      },
      "source": [
        "training_data = ListDataset(\r\n",
        "    [{\"start\": df.index[0], \"target\": df.value[:\"2015-04-05 00:00:00\"]}],\r\n",
        "    freq = \"5min\"\r\n",
        ")"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UAJMn7XVlIRJ",
        "outputId": "5f85cef4-ddb0-473e-8928-dd3de3ed3efb"
      },
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\r\n",
        "\r\n",
        "estimator = DeepAREstimator(freq=\"5min\",\r\n",
        "                            prediction_length=12,\r\n",
        "                            input_size=43,\r\n",
        "                            trainer=Trainer(epochs=15,\r\n",
        "                                            device=device))\r\n",
        "predictor = estimator.train(training_data=training_data)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "48it [00:09,  5.22it/s, avg_epoch_loss=4.75, epoch=0]\n",
            "48it [00:05,  8.98it/s, avg_epoch_loss=4.26, epoch=1]\n",
            "49it [00:05,  9.19it/s, avg_epoch_loss=4.16, epoch=2]\n",
            "47it [00:05,  8.61it/s, avg_epoch_loss=4.12, epoch=3]\n",
            "49it [00:05,  9.10it/s, avg_epoch_loss=4.09, epoch=4]\n",
            "49it [00:05,  9.22it/s, avg_epoch_loss=4.06, epoch=5]\n",
            "49it [00:05,  9.02it/s, avg_epoch_loss=4.04, epoch=6]\n",
            "49it [00:05,  9.41it/s, avg_epoch_loss=4.02, epoch=7]\n",
            "49it [00:05,  9.53it/s, avg_epoch_loss=4, epoch=8]\n",
            "49it [00:05,  8.99it/s, avg_epoch_loss=3.99, epoch=9]\n",
            "48it [00:05,  8.76it/s, avg_epoch_loss=3.98, epoch=10]\n",
            "48it [00:05,  8.97it/s, avg_epoch_loss=3.98, epoch=11]\n",
            "47it [00:05,  9.12it/s, avg_epoch_loss=3.97, epoch=12]\n",
            "48it [00:05,  9.18it/s, avg_epoch_loss=3.97, epoch=13]\n",
            "48it [00:05,  9.38it/s, avg_epoch_loss=3.96, epoch=14]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uz7NO0fDlQP_"
      },
      "source": [
        "test_data = ListDataset(\r\n",
        "    [{\"start\": df.index[0], \"target\": df.value[:\"2015-04-15 00:00:00\"]}],\r\n",
        "    freq = \"5min\"\r\n",
        ")"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "id": "PCzq27TklX5f",
        "outputId": "9690cc83-c2fc-4df4-cb71-b936beb8d6ff"
      },
      "source": [
        "for test_entry, forecast in zip(test_data, predictor.predict(test_data)):\r\n",
        "    to_pandas(test_entry)[-60:].plot(linewidth=2)\r\n",
        "    forecast.plot(color='b', prediction_intervals=[50.0, 90.0])\r\n",
        "plt.grid(which='both')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eo6CphT1ldcJ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}